Data is core to most businesses. Through data, businesses can keep track of all of their internal processes, client information, and financial situation, and be able to make sound decisions about the future. As businesses grow, new data must be incorporated, and over time the way data is formatted and processed changes. This can result in a mismatch in databases that can greatly reduce business efficiency or result in data errors and duplications.
Errors and duplications in certain industries, such as legal, or elections can result in massive issues and a loss of trust by clients and the public. Companies like BlockDrive Inc. can ensure your data is managed properly, nothing is compromised or tampered with, and help you go through a data cleaning process as needed. There are five main steps to any data cleaning that you need to know.
Step 1. Audit Your Data
The first step to any cleaning process is planning and auditing. You need to understand what kind of data you have, the current structures, where you may have duplication and errors, as well as any other anomalies. Data auditing by hand can be an arduous, if not impossible task. Using specialized software packages and data profiling makes auditing simpler. This software uses automated scripts or AI-generated audit reports to determine errors based on violations of set constraints.
Step 2. Plan Workflow Execution
Once you have completed your audit, you need to plan your workflow execution when it comes to cleaning your data. You will take multiple steps during the actual data cleaning process, so it’s good to plan it out beforehand. Thorough planning results in far fewer errors after a data cleaning.
Step 3. Execute Data Cleaning
With a proper workflow execution plan in place, you will go ahead with data cleaning. The following is an example of workflow execution.
1. Scrub for Duplicate Data: Remove duplicate data in databases, or other data sets
2. Scrub for Irrelevant Data: Remove any data that is no longer relevant to your business or the project you are working on.
3. Scrub for Incorrect Data: Amend any functions that resulted in the wrong data and remove data if needed.
4. Fix Structural Errors: Repair any structural errors that have appeared over time due to changes in processes or inconsistent data formatting. Review data collection processes.
5. Handle Missing Data: Remove missing data rows, or calculate based on observable data, or flag for missing data for manual handling later.
6. Check for Outliers: Flag outliers and check manually after data cleaning.
7. Standardize & Normalize: Standardize and normalize all remaining data to be consistent in order to properly use statistical methods and reporting with the data.
Step 4. Validation
After data cleaning, you must validate the finalized data. This is generally done by doing a new audit on the data and is necessary for quality assurance to ensure the cleaning was successful. Always validate before presenting the new data to clients or executives.
Step 5. Reporting
Produce a report on the data cleaning process. This allows fellow employees and executives to compare findings and produce fresh insights for going forward with business processes.
Data Cleaning Specialist
Whether you are handling election data or multinational corporate data, BlockDrive Inc. is here for you in order to clean your data and implement stringent data management processes. Contact us today and get real, accurate results.